Sensor Array System Based on Electronic Nose to Detect Borax in Meatballs with Artificial Neural Network

Abstract

The categorization of odors utilizing gas sensor arrays with various meatball borax concentrations has been studied. The samples included meatballs with a borax content of 0.05%, 0.10%, 0.15%, 0.20%, and 0.25% (%mm) and meatballs without any borax. Six TGS gas sensors with a baseline of 10 seconds, a detecting period of 120 seconds, and a purging period of 250 seconds make up the gas sensor array used in this work. Artificial neural networks (ANNs) and principal component analysis (PCA), which are beneficial for feature extraction and classification, are used to handle the collected data based on machine learning approaches. Two models were produced by the data analysis: model 1, which only used the PCA approach, and model 2, which only used the ANN methodology. 90.33% is the total variance value of PC from model 1. In addition, the multilayer perceptron artificial neural network (ANN-MLP) technique for model 2 yielded accuracy values of 95%.